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Resistor Emulation Approach to Low-Power RF Energy Harvesting

Paing, T., Shin, J., Zane, R., Popovic, Z.·2008·IEEE Transactions on Power Electronics

Abstract

This paper presents an approach and associated circuitry for harvesting near maximum output power from electromagnetic waves in the RF/microwave region of the spectrum with variable incident power densities in the range of tens of muW/cm2. It is shown that open loop resistor emulation at the input port of a power converter is a suitable solution for tracking the peak power point of a low-power rectifying antenna source over a wide range of incident RF power densities. A boost converter with a simple low-power control approach for resistor emulation is presented. A hardware design example with detailed efficiency analysis is given using commercially available discrete circuitry. Experimental results are presented for a system harvesting 420 muW to 8 muW from a 6 cm times 6 cm rectifying antenna with incident RF power ranging from 70 muW/cm2to 30 muW/cm2, respectively. The results demonstrate that resistor emulation is a simple and practical approach to energy harvesting with variable low-power radiative RF sources.

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Crossref · 10.1109/tpel.2008.921167

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Metadata

Authors
Paing, T., Shin, J., Zane, R., & Popovic, Z.
Venue
IEEE Transactions on Power Electronics
Published
2008-05
Volume
23
Issue
3
Pages
1494-1501
ISSN
  • 0885-8993 (ISSN-L)
  • 1941-0107
OpenAlex
W2097996630
Cited by
264
References
0
FWCI
14.34
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Resistor Emulation Approach to Low-Power RF Energy Harvesting. https://app.cochat.ai/papers/10.1109/tpel.2008.921167
The paper
Paing, T., Shin, J., Zane, R., & Popovic, Z. (2008). Resistor Emulation Approach to Low-Power RF Energy Harvesting. IEEE Transactions on Power Electronics. https://doi.org/10.1109/tpel.2008.921167

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.